Machine Learning in Modern Cancer Treatment

The many promises of AI included automated image segmentation, detection of cancerous lesions, and comparison of images. AI systems will not get tired while performing the same tasks that doctors may get tired and lose accuracy while completing. AI can also help physicians by completing tedious tasks and managing large amounts of data. Depending on the requirements of a job, a worker can either be helped by technology or in competition with it. For example, improvement in robotics can decrease opportunities for employment for workers in manufacturing but in the field of artificial intelligence and cognitive technology, it would increase the demand for workers. Recent data expresses that when considering its image and predictive analysis, AI might soon prove to be more efficient than radiologists. However, it is likely that AI will not replace the role of general physicians, but rather augment them. This is due to the fact that AI is unable to engage in interactions with patients that

Of the different challenges that AI algorithms in medical training pose, feedback and interpretability are two of the most prominent issues. Interpretability of AI algorithms is the ability of a human to understand the way it made a connection between features extracted and its predictions. A study done at Mount Sinai Hospital created a type of AI algorithm known as deep learning using data from 700,000 patients. Their algorithm was highly accurate and was able to diagnose conditions that even experts struggle to diagnose, such as  schizophrenia. However, there was no way for humans to know how the system reached a diagnosis (Paranjape, Schinkel, Nannan Panday, Car, & Nanayakkara, 2019) . This is an issue because it becomes very hard for patients to trust a system that cannot provide an explanation and if a calculation were to be made incorrectly that puts a patient in danger, then it is not known whether the doctor, the hospital, or the company that developed the AI algorithm is l